Dense Connected Edge Feature Enhancement Network for Building Edge Detection from High Resolution Remote Sensing Imagery
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Bibliographic record
Abstract
Deep-learning-based methods for building-edge-detection have been widely researched and applied in the field of image processing.However, these methods often emphasis the analysis of deep features, which may result in neglecting crucial shallow information representation.Furthermore, abstract features in the deep layers can potentially interfere with the accuracy of edge extraction.To address these challenges, we propose a densely connected edge-detection enhancement network (DCEFE-Net) for building-edge-detection in high-resolution remote sensing images.Firstly, by introducing spatial land channel attention modules, we effectively captured low-level spatial information and high-level semantic information from the input image.Secondly, the proposed edge-aware feature enhancement (EAFE) module emphasis the representation of informative edge features.By alliteratively generating multiple layers of edge-detection maps, it addresses the issue of edge detail loss and enhances edge-detection accuracy.Finally, the dense connectivity blocks strengthen the connections between the convolutional layers, thereby preventing the loss of edge features.Experimental results on the WHU and the Inria Aerial Image Labeling datasets validate the effectiveness of DCEFE-Net, as it consistently produces clear and reliable building-edge results. RSUMLes m ethodes bas ees sur l'apprentissage profond pour la d etection des contours de btiments ont fait l'objet de nombreuses recherches et applications dans le domaine du traitement d'images.Cependant, ces m ethodes mettent souvent l'accent sur l'analyze des caract eristiques profondes, ce qui peut conduire a n egliger la repr esentation de l'information superficielle.De plus, les caract eristiques abstraites dans les couches profondes peuvent potentiellement inter-f erer avec la pr ecision de l'extraction des contours.Pour relever ces d efis, nous proposons un r eseau dens ement connect e de rehaussement de la d etection de contours (DCEFE-Net) pour la d etection des contours des btiments dans les images de t el ed etection a haute r esolution.Tout d'abord, en introduisant des modules d'attention pour les canaux spatiaux, nous avons captur e efficacement des informations spatiales de bas niveau et des informations s emantiques de haut niveau a partir de l'image d'entr ee.Deuxi emement, le module propos e de rehaussement des caract eristiques tient compte des artes (EAFE) et met l'accent sur la repr esentation des particularit es informatives des contours.En g en erant par allit erations plusieurs couches de cartes de d etection des contours, le module r esout le probl eme de la perte de d etails aux contours et am eliore la pr ecision de leur d etection.Enfin, les blocs de connectivit e denses renforcent les connexions entre les couches convolutives, empchant ainsi la perte des particularit es des contours.Les r esultats exp erimentaux sur les jeux de donn ees WHU et Inria Aerial Image Labeling valident l'efficacit e du DCEFE-Net, car il produit syst ematiquement des r esultats clairs et fiables en bordure de btiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it